(Electronic ambient music) >> Announcer: Live from Nassau in the Bahamas, it's The Cube! Covering Polycon 18, brought to you by PolyMath. >> We are live here with The Cube's exclusive coverage at Polycon 18. It's a securitized token conference, but really, it's about cryptography, cryptocurrency, blockchain, token economics. The whole community's here, investors, entrepreneurs, and startups. We have two great guests here from CryptoChicks, Nataliya Hearn and Natalia Ameline. Pioneers in the industry doing something really compelling, the first ever blockchain hackathon coming up in April. It's historic, it's the first. Welcome to the Cube, thanks for joining me. >> Thank you for having us. >> Thank you for having us. >> So I love the t-shirts, CryptoChicks, I want one, a few. Can I buy them on the website? Can I get them made? >> Yeah, you can, absolutely >> I want my daughters to have those immediately, so. People in our community know that the Cube's really been... we love women in tech because there are so many smart women out there and it's awesome to showcase. But beyond that, it's this real technology being innovated. Talk about what you guys are doing. You have a really important mission, had great success, with CryptoChicks. This is like a movement inside this community, but it's also happening all around the world. You guys have big plans. Take a minute to explain the group, how you guys are operating, how it's going, and talk about this big event. >> We started this group because we realized that women are underrepresented in the space, and you don't need to go far; look at what's going on at this conference, right? Even though we are pleased towards the increase in turnaround of women, in events like this, but we still have ways to improve. So we started this group CryptoChicks with the sole mission to increase, improve gender balance, and increase participation of women in the community. And we're doing it in a variety of ways, but largely what we try to do is we try to create an environment where women feels safe to learn. It's small classes. Where women come in, they can ask questions, they can feel at ease, and I think it's very important because not every woman feels comfortable getting up in a big crowd and asking questions. And I think what we do is really helpful for a lot of women this way. >> It's very inspiring. Also you guys as co-founders Nataliya we were talking about you were a professor, and education's a big part of it, but also human nature right? So talk about the dynamic and how you guys approached that because there's different styles, both men and women and we got to kind of get it going together, I mean, you guys have got to get critical mass. Now the good news is, people are talking about it, and it's happening, and... >> Absolutely, I think, kind of knowledge. People hear stuff. You know I had kind of interesting... I was talking to a woman who was in tech but her English wasn't great, and all this kind of stuff. So she called it BigCoin, which I love it. (laughs) Because it is kind of a BigCoin you know? Out of all the coins it's the biggest coin. So stuff like this. If you go to meet ups you would have in a room of a hundred, maybe one or two women. And then they'll go, well what's a wallet? What is all this about? Just even the basic, baby-stepping, through the system. And then I think well we're focusing on only one part of it. The other part of it is that we're creating a really new level of democracy. And that element, I think, that's why we need the education. An education probably, while women is great, but we've got to start a little earlier. The interests should come at least in high school level, where you go well, What is debt? What is value? How do you define currency? Actually all the stuff we're doing at the conference here, in terms of securities. Is it a security? Or is it not a security? How do you define? So all of that starts early on. >> I've been having conversations at many levels about this, at Sundance Film Festival we talk about the role of technology. So it used to be, you know, the Boys Club. That's now changing, which is great, but also there's a trend of multidisciplinary things. You mentioned economics and all these things. So the world now is becoming integrated. So math for instance, there's a lot of math geeks out there, male and female. You don't have to be a coder per say, right? There's certainly more coding opportunities, for women, but it's not just one thing. You can do anything. Fifty percent of the population is women. If this is going to change the world, which it is. Fifty percent of it is going to be impacted too. So they have to have a role in what's going on in the community. So it's natural it should happen, I mean... >> Absolutely. And actually one of the reasons the Hackathon, the reason it's first all women Hackathon in Blockchain, and we actually have two streams. And one stream is for hackers, who are into the nitty-gritty of, sort of, the coding part, and we actually have support for them as well, in terms of learning. And then we also have the business track, where if you have an idea, and you think that Blockchain would be a really good avenue to take that idea, so you could pitch your idea during the Hackathon as well. >> And just to clarify, this is the up and coming Hackathon that you guys are doing. All women. What's the date? Share the details. Share the details. >> So it's going to be actually a conference and Hackathon, we're going to run it parallel. Conference will start on the 6th of April and going through the 8th of April, and the Hackathon will happen at the same time. >> And where is the conference going to be attended? >> So the conference is taking place in Toronto, we're partnering with our venue partner MaRS Discovery District. So it's an absolutely amazing venue in Toronto. And also our partner MaRS has a history of, you know, promoting the women in technology. So it's a good partnership for us. And it's going to be, the Hackathon is going to run about thirty hours and hopefully it's going to be a lot of good connections coming out of it. I think one of the things that we want to accomplish in this Hackathon for women is to make it easy for them to get opportunities. So most importantly we want to connect them with employers. And that's a great venue for that, because when we travel, we have a lot of the times owners of the companies will approach us and say you know, we're really looking to diversify our team. Can you help us? Because women just don't apply. I think that's another way we're trying to really infuse more women into the community. >> Open up channels of opportunity, it's not just having it be like a job interview. >> Exactly. >> So networking, demonstrating skills, style. Are you guys seeing the formula that works with people, with women? Because we see different conversations around this, you know. Take a certain approach, posture this way, be different. Eventually, I interview a lot of women that are saying, I'm going to be hardcore and some say, I just want to wear high heels and I'm a fashion person, that's who I am and why would I want to change that just because I'm a woman? So there's different views on this. Is there any pattern, or formula that you would suggest or observe? >> You know I think we live in a really fortunate part of the globe where we can actually do what we want to do. There aren't too many places like that in the world. And I think that we've got to be really thankful for that, and then it really is, you know, we are empowered to create opportunities. And in this space, it's a really young space. I mean it's really fundamental. Some people say well we've been in it for ten years. Really, most of the people have been in it for, you know, couple years. So don't think, women shouldn't think that well, there's all these guy and they know what they're doing. They also don't know what they're doing, everything's changing. Every wallet and every structure that is being created today is going to be a little different tomorrow, it's a process. >> If you say you're an expert about something here, then you're really a pretender because everyone's always learning. And the real pros are humble about that. So that's one observation. But the other one is, and I want to get your reaction on this because I go to a lot of events. Especially in tech. Where a lot of male-dominated, you know, enterprise here and there. This community's very mission oriented and I don't see any signs of lack of inclusion. So I think the door is open at least my perspective, and certainly we've been covering a lot in the space, Bitcoins in 2010 and crypto and everything else. But being here I see open doors. I can say the other verticals, not so much. Here, it seems open. Do you guys agree with that? What's good about that if you do agree, how do people walk through those doors? And if it's not, what needs to happen? What's your observation? >> I think it depends on the personalities a lot. I find that some personalities, the door is open, and will just walk in. Some personalities are, you know, I want someone to bring me and introduce me, I think it's like this everywhere. I think in this space I mostly see that it's friendly space, pretty happy with it, but I also think there could be some improvements, because quite frankly sometimes the culture is not necessarily that welcoming. For example, you go to the chatrooms on Facebook as an example. A woman makes a comment and after that you'll see lines of guys responding, what are you doing here? And why did you say that? >> Really? >> Yeah it's very common >> It's IRC culture, really. >> Yeah, so it's you know, some women are perfectly fine with it, right? And for me, it's like okay, you know, everybody's entitled to opinions. But some next time would not comment, right? And I don't know, maybe guys have a little bit thicker skin, and they take some ridicule better, I don't know, but I think there's still ways to make the culture a little bit more open and I guess comforted. >> Nataliya, do you agree with that? What's your take on that? >> I think it really starts with upbringing, again, and how we raise our children. I have 3 sons, so I raise them in the way I'd want to be treated, in an environment. I'm an engineer, so I've worked with men all my life, and this is not unusual for me. I've gone to conferences all my life, thousands of people, twenty women. >> Yeah you've got a thick skin, you guys have thick skin. >> And you know, in a way yeah, it takes guts, like you said before, to wear high heels and a skirt and really stand out when you're already standing out. So you've got to put your head up you know? And you walk into that room. >> Be yourself! Right? But don't be afraid. I guess what you're saying is, you could have whatever posture you want to have, just be proud, keep your chin up, as they say. Alright, so let's talk about, you mentioned, you guys are moms. So like, I have four kids too. Two daughters, David Vellante has four as well, the same. These kids that are born now are growing with digital natives, some are kind of pre, post Facebook, pre Instagram, Snapchat, it ranges in the spectrum. Certainly gaming has been a big part of the culture of the youth. So people who are digital natives, and or have come on with the connected social world that is, they are doing things differently. So I wanted to get your thoughts as parents, I get asked the question a lot: should I let him game? Should I let him code? What should I do? What's good? What's bad? There's no data other than kind of anecdotal or vision. I personally believe in gaming as a good future of work scenario, as long as you don't OD on it, and overdose on too much gaming. I think coding is the same. So I think this is going to be the tooling of the future, what do you guys think as parents about the exposure of technology? How do you do it? Is there a diet? Is there a recipe? I mean, what do you guys think? >> I think personally it's great. I think the younger kids get exposed to technology, the more comfortable they feel with it, and the more likely they are to become the next, you know, Steve Jobs and Bill Gates etcetera. And I think our society, whether some people like it or not, it's moving in a direction where we're becoming more and more technology addicted and dependent on it. Technology is everywhere, we don't even realize, that it's there. You know, you wake up in the morning and you look at the internet. You may like it or not, but that's the lifestyle these days. So I think for me, with kids, we need to give them freedom, and we need to observe. Because at the end of the day, I think kids are intuitive, they know what they're interested in, and we need to help them nurture their interests, so that they grow up, and they don't need to go to a job that they hate. Instead they do what they love. And that's how we're becoming a more productive society. >> And the learning online too is an opportunity to go nonlinear. Learn things at the scale you don't have to wait for the next class or semester. Your thoughts on this, Nataliya? >> Absolutely, I think every child has a gift, and I think it's parents responsibility to discover that gift. Instead of shoving your ideas, or things you didn't achieve in life into your children. >> That's called snowplow parent or helicopter parenting. >> So absolutely, and we are a technology-driven society, and you know, I'm an engineer so I'm a techie, so I've introduced my sons to a lot of things, but you know what? They've introduced me, and actually they kept me in this sector. >> I think the observational thing is really important. Freedom with observation. That's not monitoring, and surveillance, or helicoptering. It's really like, let him play, let him explore, let them have a good time. Understand it, but be mindful of what you're observing. And that's key. >> And yeah, too much of anything is not good. You know, you have to balance your sleep patterns, and all this kind of stuff, all of that has to come into a child's life. >> Yeah, intervention is required at some point, you know, when you see that the kid is shaking. (laughing) >> I always say to women in tech who are moms like, man, you have it so easy now, because you know how hard it is to raise children. Being a parent is super hard, and a lot of people look at that, need to understand that's how hard it is. It's really a wonderful thing. So thanks for sharing. Looking forward to following the CryptoChicks and covering the Hackathon, so let us know how it goes. Are there going to be any live feeds, or twitter handles, or hashtag, what's going on? >> There will be, and we'll let you know. Thank you for the opportunity >> Thank you very much >> Thank you very much for sharing, CryptoChicks here on The Cube, I'm John Furrier. Live coverage continuing, day two, of SiliconANGLE Media's Cube exclusive coverage at Polycon 18. We'll be right back. (Electronic music).

>> (Narrator) Live from Madrid, Spain. It's The Cube covering HP Discover Madrid 2017, brought to you by Hewlett Packard Enterprise. >> We're back at HPE Discover Madrid 2017. This is The Cube, the leader in live tech coverage. My name is Dave Vellante and I'm with my co-host for the week, Peter Burris. Cat Graves is here, she's a research scientist at Hewlett Packard Enterprises. And she's joined by Natalia Vassilieva. Cube alum, senior research manager at HPE. Both with the labs in Palo Alto. Thanks so much for coming on The Cube. >> Thank you for having us. >> You're welcome. So for decades this industry has marched to the cadence of Moore's Law, bowed down to Moore's Law, been subservient to Moore's Law. But that's changing, isn't it? >> Absolutely. >> What's going on? >> I can tell Moore's Law is changing. So we can't increase the number, of course, on the same chip and have the same space. We can't increase the density of the computer today. And from the software perspective, we need to analyze more and more data. We are now marching calls into the area of artificial intelligence when we need to train larger and larger models, we need more and more compute for that. And the only possible way today to speed up the training of those modules, to actually enable the AI, is to scale out. Because we can't put more cores on the chip. So we try to use more chips together But then communication bottlenecks come in. So we can't efficiently use all of those chips. So for us on the software side, on the part of people who works how to speed up the training, how to speed up the implementation of the algorithms, and the work of those algorithms, that's a problem. And that's where Cat can help us because she's working on a new hardware which will overcome those troubles. >> Yeah, so in our lab what we do is try and think of new ways of doing computation but also doing the computations that really matter. You know, what are the bottlenecks for the applications that Natalia is working on that are really preventing the performance from accelerating? Again exponentially like Moore's Law, right? We'd like to return to Moore's Law where we're in that sort of exponential growth in terms of what compute is really capable of. And so what we're doing in labs is leveraging novel devices so, you've heard of memristor in the past probably. But instead of using memristor for computer memory, non volatile memory for persistent memory driven computer systems, we're using these devices instead for doing computation itself in the analog domain. So one of our first target applications, and target core computations that we're going after is matrix multiplication. And that is a fundamental mathematical building block for a lot of different machine learning, deep learning, signal processing, you kind of name it, it's pretty broad in terms of where it's used today. >> So Dr. Tom Bradicich was talking about the dot product, and it sounds like it's related. Matrix multiplications, suddenly I start breaking out in hives but is that kind of related? >> That's exactly what it is. So, if you remember your linear algebra in college, a dot product is exactly a matrix multiplication. It's the dot in between the vector and the matrix. The two itself, so exactly right. Our hardware prototype is called the dot product engine. It's just cranking out those matrix multiplications. >> And can you explain how that addresses the problem that we're trying to solve with respect to Moore's Law? >> Yeah, let me. You mentioned the problem with Moore's Law. From me as a software person, the end of Moore's Law is a bad thing because I can't increase their compute power anymore on the single chip. But for Cat it's a good thing because it forced her to think what's unconventional. >> (Cat) It's an opportunity. >> It's an opportunity! >> It forced her to think, what are unconventional devices which she can come up with? And we also have to mention they understand that general purpose computing is not always a solution. Sometimes if you want to speed up the thing, you need to come up with a device which is designed specifically for the type of computation which you care about. And for machine learning technification, again as I've mentioned, these matrix-matrix multiplications matrix-vector multiplications, these are the core of it. Today if you want to do those AI type applications, you spend roughly 90% of the time doing exactly that computation. So if we can come up with a more power efficient and a more effective way of doing that, that will really help us, and that's what dot product engine is solving. >> Yes, an example some of our colleagues did in architectural work. Sort of taking the dot product engine as the core, and then saying, okay if I designed a computer architecture specifically for doing convolutional neural networks. So image classification, these kinds of applications. If I built this architecture, how would it perform? And how would it compare to GPUs? And we're seeing 10 to 100 X speed up over GPUs. And even 15 X speed up over if you had a custom-built, state of the art specialized digital Asic. Even comparing to the best that we can do today, we are seeing this potential for a huge amount of speed up and also energy savings as well. >> So follow up on that, if I may. So you're saying these alternative processors like GPUs, FGPAs, custom Asics, can I infer from that that that is a stop-gap architecturally, in your mind? Because you're seeing these alternative processors pop up all over the place. >> (Cat) Yes. >> Is that a fair assertion? >> I think that recent trends are obviously favoring a return to specialized hardware. >> (Dave) Yeah, for sure. Just look at INVIDIA, it's exploding. >> I think it really depends on the application and you have to look at what the requirements are. Especially in terms of where there's a lot of power limitations, right, GPUs have become a little bit tricky. So there's a lot of interest in the automotive industry, space, robotics, for more low power but still very high performance, highly efficient computation. >> Many years ago when I was actually thinking about doing computer science and realized pretty quickly that I didn't have the brain power to get there. But I remember thinking in terms of there's three ways of improving performance. You can do it architecturally, what do you do with an instruction? You can do it organizationally, how do you fit the various elements together? You can do it with technology, which is what's the clock speed, what's the underlying substrate? Moore's Law is focused on the technology. Risk, for example, focused on architecture. FPGAs, arm processors, GPUs focus on architecture. What we're talking about to get back to that doubling the performance every 18 months from a computing standpoint not just a chip standpoint, now we're talking about revealing and liberating, I presume, some of the organization elements. Ways of thinking about how to put these things together. So even if we can't get improvements that we've gotten out of technology, we can start getting more performance out of new architectures. But organizing how everything works together. And make it so that the software doesn't have to know, or the developer, doesn't have to know everything about the organization. Am I kind of getting there with this? >> Yes, I think you are right. And if we are talking about some of the architectural challenges of today's processors, not only we can't increase the power of a single device today, but even if we increase the power of a single device, then the challenge would be how do you bring the data fast enough to that device? So we will have problems with feeding that device. And again, what dot product engine does, it does computations in memory, inside. So you limit the number of data transfers between different chips and you don't face the problem of feeding their computation thing. >> So similar same technology, different architecture, and using a new organization to take advantage of that architecture. The dot product engine being kind of that combination. >> I would say that even technology is different. >> Yeah, my view of it we're actually thinking about it holistically. We have in labs software working with architects. >> I mean it's not just a clock speed issue. >> It's not just a clock speed issue. It's thinking about what computations actually matter, which ones you're actually doing, and how to perform them in different ways. And so one of the great things as well with the dot product engine and these kind of new computation accelerators, is with something like the memory driven computing architecture. We have now an ecosystem that is really favoring accelerators and encouraging the development of these specialized hardware pieces that can kind of slot in in the same architecture that can scale also in size. >> And you invoke that resource in an automated way, presumably. >> Yeah, exactly. >> What's the secret sauce behind that? Is that software that does that or an algorithm that chooses the algorithm? >> A gen z. >> A gen z's underlying protocol is to make the device talk to the data. But at the end of the system software, it's algorithms also which will make a decision at every particular point which compute device I should use to do a particular task. With memory driven computing, if all my data sits in the shared pool of memory and I have different heterogeneous compute devices, being able to see that data and to talk to that data, then it's up to the system management software to allocate the execution of a particular task to the device which does that the best. In a more power efficient way, in the fastest way, and everybody wins. >> So as a software person, you now with memory driven computing have been thinking about developing software in a completely different way. Is that correct? >> (Natalia) Yeah. You're not thinking about going through I/O stack anymore and waiting for a mechanical device and doing other things? >> It's not only the I/O stack. >> As I mentioned today, the only possibility for us to decrease the time of processing for the algorithms is to scale out. That means that I need to take into account the locality of the data. It's not only when you distribute the computation across multiple nodes, even if we have some number based which is we have different sockets in a single system. With local memory and the memory which is remote to that socket but which is local to another socket. Today as a software programmer, as a developer, I need to take into account where my data sits. Because I know in order to accept the data on a local memory it'll take me 100 seconds to accept my data. In the remote socket, it will take me longer. So when I developed the algorithm in order to prevent my computational course to stall and to wait for the data, I need to schedule that very carefully. With memory driven computing, giving an assumption that, again, all memory not only in the single pool, but it's also evenly accessible from every compute device. I don't need to care about that anymore. And you can't even imagine such a relief it is! (laughs) It makes our life so much easier. >> Yeah, because you're spending a lot of time previously trying to optimize your code >> Yes for that factor of the locality of the data. How much of your time was spent doing that menial task? >> Years! In the beginning of Moore's Law and the beginning of the traditional architectures, if you turn to the HPC applications, every HPC application device today needs to take care of data locality. >> And you hear about when a new GPU comes out or even just a slightly new generation. They have to take months to even redesign their algorithm to tune it to that specific hardware, right? And that's the same company, maybe even the same product sort of path lined. But just because that architecture has slightly changed changes exactly what Natalia is talking about. >> I'm interested in switching subjects here. I'd love to spend a minute on women in tech. How you guys got into this role. You're both obviously strong in math, computer backgrounds. But give us a little flavor of your background, Cat, and then, Natalia, you as well. >> Me or you? >> You start. >> Hm, I don't know. I was always interested in a lot of different things. I kind of wanted to study and do everything. And I got to the point in college where physics was something that still fascinated me. I felt like I didn't know nearly enough. I felt like there was still so much to learn and it was constantly challenging me. So I decided to pursue my Ph.D in that, and it's never boring, and you're always learning something new. Yeah, I don't know. >> Okay, and that led to a career in technology development. >> Yeah, and I actually did my Ph.D in kind of something that was pretty different. But towards the end of it, decided I really enjoyed research and was just always inspired by it. But I wanted to do that research on projects that I felt like might have more of an impact. And particularly an impact in my lifetime. My Ph.D work was kind of something that I knew would never actually be implemented in, maybe a couple hundred years or something we might get to that point. So there's not too many places, at least in my field in hardware, where you can be doing what feels like very cutting edge research, but be doing it in a place where you can see your ideas and your work be implemented. That's something that led me to labs. >> And Natalia, what's your passion? How did you arrive here? >> As a kid I always liked different math puzzles. I was into math and pretty soon it became obvious that I like solving those math problems much more than writing about anything. I think in middle school there was the first class on programming, I went right into that. And then the teacher told me that I should probably go to a specialized school and that led me to physics and mathematics lyceum and then mathematical department at the university so it was pretty straightforward for me since then. >> You're both obviously very comfortable in this role, extremely knowledgeable. You seem like great leaders. Why do you feel that more women don't pursue a career in technology. Do you have these discussions amongst yourselves? Is this something that you even think about? >> I think it starts very early. For me, both my parents are scientists, and so always had books around the house. Always was encouraged to think and pursue that path, and be curious. I think its something that happens at a very young age. And various academic institutions have done studies and shown when they do certain things, its surmountable. Carnegie Mellon has a very nice program for this, where they went for the percentage of women in their CS program went from 10% to 40% in five years. And there were a couple of strategies that they implemented. I'm not gonna get all of them, but one was peer to peer mentoring, when the freshmen came in, pairing them with a senior, feeling like you're not the only one doing what you're doing, or interested in what you're doing. It's like anything human, you want to feel like you belong and can relate to your group. So I think, yeah. (laughs) >> Let's have a last word. >> On that topic? >> Yeah sure, or any topic. But yes, I'm very interested in this topic because less than 20% of the tech business is women. Its 50W% of the population. >> I think for me its not the percentage which matters Just don't stay in the way of those who's interested in that. And give equal opportunities to everybody. And yes, the environment from the very childhood should be the proper one. >> Do you feel like the industry gives women equal opportunity? >> For me, my feeling would be yes. You also need to understand >> Because of your experience Because of my experience, but I also originally came from Russia, was born in St. Petersburg, and I do believe that ex-Soviet Union countries has much better history in that. Because the Soviet Union, we don't have man and woman. We have comrades. And after the Second World War, there was women who took all hard jobs. And we used to get moms at work. All moms of all my peers have been working. My mom was an engineer, my dad is an engineer. From that, there is no perception that the woman should stay at home, or the woman is taking care of kids. There is less of that. >> Interesting. So for me, yes. Now I think that industry going that direction. And that's right. >> Instructive, great. Well, listen, thanks very much for coming on the Cube. >> Sure. >> Sharing the stories, and good luck in lab, wherever you may end up. >> Thank you. >> Good to see you. >> Thank you very much. >> Alright, keep it right there everybody. We'll be back with our next guest, Dave Vallante for Peter Buress. We're live from Madrid, 2017, HPE Discover. This is the Cube.

>> Announcer: Live from Las Vegas, it's the CUBE! Covering HPE Discover 2017. Brought to you by Hewlett Packard Enterprise. >> Hey, welcome back, everyone. We are live here in Las Vegas for SiliconANGLE Media's CUBE exclusive coverage of HPE Discover 2017. I'm John Furrier, my co-host, Dave Vellante. Our next guest is Kirk Bresniker, fellow and VP chief architect of Hewlett Packard Labs, and Natalia Vassilieva, senior research manager, Hewlett Packard Labs. Did I get that right? >> Yes! >> John: Okay, welcome to theCUBE, good to see you. >> Thank you. >> Thanks for coming on, really appreciate you guys coming on. One of the things I'm most excited about here at HPE Discover is, always like to geek out on the Hewlett Packard Labs booth, which is right behind us. If you go to the wide shot, you can see the awesome display. But there's some two things in there that I love. The Machine is in there, which I love the new branding, by the way, love that pyramid coming out of the, the phoenix rising out of the ashes. And also Memristor, really game-changing. This is underlying technology, but what's powering the business trends out there that you guys are kind of doing the R&D on is AI, and machine learning, and software's changing. What's your thoughts as you look at the labs, you look out on the landscape, and you do the R&D, what's the vision? >> One of the things what is so fascinating about the transitional period we're in. We look at the kind of technologies that we've had 'til date, and certainly spent a whole part of my career on, and yet all these technologies that we've had so far, they're all kind of getting about as good as they're going to get. You know, the Moore's Law semiconductor process steps, general-purpose operating systems, general-purpose microprocessors, they've had fantastic productivity growth, but they all have a natural life cycle, and they're all maturing. And part of The Machine research program has been, what do we think is coming next? And really, what's informing us as what we have to set as the goals are the kinds of applications that we expect. And those are data-intensive applications, not just petabytes, exabytes, but zettabytes. Tens of zettabytes, hundreds of zettabytes of data out there in all those sensors out there in the world. And when you want to analyze that data, you can't just push it back to the individual human, you need to employ machine learning algorithms to go through that data to call out and find those needles in those increasingly enormous haystacks, so that you can get that key correlation. And when you don't have to reduce and redact and summarize data, when you can operate on the data at that intelligent edge, you're going to find those correlations, and that machine learning algorithm is going to be that unbiased and unblinking eye that's going to find that key relationship that'll really have a transformational effect. >> I think that's interesting. I'd like to ask you just one follow-up question on that, because I think, you know, it reminds me back when I was in my youth, around packets, and you'd get the buffer, and the speeds, and feeds. At some point there was a wire speed capability. Hey, packets are moving, and you can do all this analysis at wire speed. What you're getting at is, data processing at the speed of, as fast as the data's coming in and out. Is that, if I get that right, is that kind of where you're going with this? Because if you have more data coming, potentially an infinite amount of data coming in, the data speed is going to be so high-velocity, how do you know what a needle looks like? >> I think that's a key, and that's why the research Natalia's been doing is so fundamental, is that we need to be able to process that incredible amount of information and be able to afford to do it. And the way that you will not be able to have it scale is if you have to take that data, compress it, reduce it, select it down because of some pre-determined decision you've made, transmit it to a centralized location, do the analysis there, then send back the action commands. Now, we need that cycle of intelligence measurement, analysis and action to be microseconds. And that means it needs to happen at the intelligent edge. I think that's where the understanding of how machine learning algorithms, that you don't program, you train, so that they can work off of this enormous amount of data, they voraciously consume the data, and produce insights. That's where machine learning will be the key. >> Natalia, tell us about your research on this area. Curious. Your thoughts. >> We started to look at existing machine learning algorithms, and whether their limiting factors today in the infrastructure which don't allow to progress the machine learning algorithms fast enough. So, one of the recent advances in AI is appearance, or revival, of those artificial neural networks. Deep learning. That's a very large hype around those types of algorithms. Every speech assistant which you get, Siri in your phone, Cortana, or whatever, Alexa by Amazon, all of them use deep learning to train speech recognition systems. If you go to Facebook and suddenly it starts you to propose to mark the faces of your friends, that the face detection, face recognition, also that was deep learning. So that's a revival of the old artificial neural networks. Today we are capable to train byte-light enough models for those types of tasks, but we want to move forward. We want to be able to process larger volumes of data, to find more complicated patterns, and to do that, we need more compute power. Again, today, the only way how you can add more compute power to that, you scale out. So there is no compute device on Earth today which is capable to do all the computation. You need to have many of them interconnect together, and they all crunch numbers for the same problem. But at some point, the communication between those nodes becomes a bottleneck. So you need to let know laboring node what you achieved, and you can't scale out anymore. Adding yet another node to the cluster won't lead up to the reduction of the training time. With The Machine, when we have added the memory during computing architecture, when all data seeds in the same shared pool of memory, and when all computing nodes have an ability to talk to that memory. We don't have that limitation anymore. So for us, we are looking forward to deploy those algorithms on that type of architecture. We envision significant speedups in the training. And it will allow us to retrain the model on the new data, which is coming. To do not do training offline anymore. >> So how does this all work? When HP split into two companies, Hewlett Packard Labs went to HPE and HP Labs went to HP Ink. So what went where, and then, first question. Then second question is, how do you decide what to work on? >> I think in terms of how we organize ourselves, obviously, things that were around printing and personal systems went to HP Ink. Things that were around analytics, enterprise, hardware and research, went to Hewlett Packard Labs. The one thing that we both found equally interesting was security, 'cause obviously, personal systems, enterprise systems, we all need systems that are increasingly secure because of the advanced, persistent threats that are constantly assaulting everything from our personal systems up through enterprise and public infrastructure. So that's how we've organized ourselves. Now in terms of what we get to work on, you know, we're in an interesting position. I came to Labs three years ago. I used to be the chief technologist for the server global business unit. I was in the world of big D, tiny R. Natalia and the research team at Labs, they were out there looking out five, 10, 15, or 20 years. Huge R, and then we would meet together occasionally. I think one of the things that's happened with our machine advanced development and research program is, I came to Labs not to become a researcher, but to facilitate that communication to bring in the engineering, the supply chain team, that technical and production prowess, our experience from our services teams, who know how things actually get deployed in the real world. And I get to set them at the bench with Natalia, with the researchers, and I get to make everyone unhappy. Hopefully in equal amounts. That the development teams realize we're going to make some progress. We will end up with fantastic progress and products, both conventional systems as well as new systems, but it will be a while. We need to get through, that's why we had to build our prototype. To say, "No, we need a construction proof of these ideas." The same time, with Natalia and the research teams, they were always looking for that next horizon, that next question. Maybe we pulled them a little bit closer, got a little answers out of them rather than the next question. So I think that's part of what we've been doing at the Labs is understanding, how do we organize ourselves? How do we work with the Hewlett Packard Enterprise Pathfinder program, to find those little startups who need that extra piece of something that we can offer as that partnering community? It's really a novel approach for us to understand how do we fill that gap, how do we still have great conventional products, how do we enable breakthrough new category products, and have it in a timeframe that matters? >> So, much tighter connection between the R and the D. And then, okay, so when Natalia wants to initiate a project, or somebody wants Natalia to initiate a project around AI, how does that work? Do you say, "Okay, submit an idea," and then it goes through some kind of peer review? And then, how does it get funded? Take us through that. >> I think I'll give my perspective, I would love to hear what you have from your side. For me, it's always been organic. The ideas that we had on The Machine, for me, my little thread, one of thousands that's been brought in to get us to this point, started about 2003, where we were getting ready for, we're midway through Blade Systems C-class. A category-defining product. A absolute home run in defining what a Blade system was going to be. And we're partway through that, and you realize you got a success on your hands. You think, "Wow, nothing gets better than this!" Then it starts to worry, what if nothing gets better than this? And you start thinking about that next set of things. Now, I had some insights of my own, but when you're a technologist and you have an insight, that's a great feeling for a little while, and then it's a little bit of a lonely feeling. No one else understands this but me, and is it always going to be that way? And then you have to find that business opportunity. So that's where talking with our field teams, talking with our customers, coming to events like Discover, where you see business opportunities, and you realize, my ingenuity and this business opportunity are a match. Now, the third piece of that is someone who can say, a business leader, who can say, "You know what?" "Your ingenuity and that opportunity can meet "in a finite time with finite resources." "Let's do it." And really, that's what Meg and leadership team did with us on The Machine. >> Kirk, I want to shift gears and talk about the Memristor, because I think that's a showcase that everyone's talking about. Actually, The Machine has been talked about for many years now, but Memristor changes the game. It kind of goes back to old-school analog, right? We're talking about, you know, login, end-login kind of performance, that we've never seen before. So it's a completely different take on memory, and this kind of brings up your vision and the team's vision of memory-driven computing. Which, some are saying can scale machine learning. 'Cause now you have data response times in microseconds, as you said, and provisioning containers in microseconds is actually really amazing. So, the question is, what is memory-driven computing? What does that mean? And what are the challenges in deep learning today? >> I'll do the machine learning-- >> I will do deep learning. >> You'll do the machine learning. So, when I think of memory-driven computing, it's the realization that we need a new set of technologies, and it's not just one thing. Can't we just do, dot-dot-dot, we would've done that one thing. This is more taking a holistic approach, looking at all the technologies that we need to pull together. Now, memories are fascinating, and our Memristor is one example of a new class of memory. But they also-- >> John: It's doing it differently, too, it's not like-- >> It's changing the physics. You want to change the economics of information technology? You change the physics you're using. So here, we're changing physics. And whether it's our work on the Memristor with Western Digital and the resistive RAM program, whether it's the phase-change memories, whether it's the spin-torque memories, they're all applying new physics. What they all share, though, is the characteristic that they can continue to scale. They can scale in the layers inside of a die. The die is inside of a package. The package is inside of a module, and then when we add photonics, a transformational information communications technology, now we're scaling from the package, to the enclosure, to the rack, cross the aisle, and then across the data center. All that memory accessible as memory. So that's the first piece. Large, persistent memories. The second piece is the fabric, the way we interconnect them so that we can have great computational, great memory, great communication devices available on industry open standards, that's the Gen-Z Consortium. The last piece is software. New software as well as adapting existing productive programming techniques, and enabling people to be very productive immediately. >> Before Natalia gets into her piece, I just want to ask a question, because this is interesting to me because, sorry to get geeky here, but, this is really cool because you're going analog with signaling. So, going back to the old concepts of signaling theory. You mentioned neural networks. It's almost a hand-in-glove situation with neural networks. Here, you have the next question, which is, connect the dots to machine learning and neural networks. This seems to be an interesting technology game-changer. Is that right? I mean, am I getting this right? What's this mean? >> I'll just add one piece, and then hear Natalia, who's the expert on the machine learning. For me, it's bringing that right ensemble of components together. Memory technologies, communication technologies, and, as you say, novel computational technologies. 'Cause transistors are not going to get smaller for very much longer. We have to think of something more clever to do than just stamp out another copy of a standard architecture. >> Yes, you asked about changes of deep learning. We look at the landscape of deep learning today, and the set of tasks which are solved today by those problems. We see that although there is a variety of tasks solved, most of them are from the same area. So we can analyze images very efficiently, we can analyze video, though it's all visual data, we can also do speech processing. There are few examples in other domains, with other data types, but they're much fewer. It's much less knowledge how to, which models to train for those applications. The thing that one of the challenges for deep learning is to expand the variety of applications which it can be used. And it's known that artificial neural networks are very well applicable to the data where there are many hidden patterns underneath. And there are multi-dimensional data, like data from sensors. But we still need to learn what's the right topology of neural networks to do that. What's the right algorithm to train that. So we need to broaden the scope of applications which can take advantage of deep learning. Another aspect is, which I mentioned before, the computational power of today's devices. If you think about the well-known analogy of artificial neural network in our brain, the size of the model which we train today, the artificial neural networks, they are much, much, much smaller than the analogous thing in our brain. Many orders of magnitude. It was shown that if you increase the size of the model, you can get better accuracy for some tasks. You can process a larger variety of data. But in order to train those large models, you need more data and you need more compute power. Today, we don't have enough compute power. Actually did some computation, though in order to train a model which is comparable in size with our human brain, you will need to train it in a reasonable time. You will need a compute device which is capable to perform 10 to the power of 26 floating-point operations per second. We are far, far-- >> John: Can you repeat that again? >> 10 to the power of 26. We are far, far below that point now. >> All right, so here's the question for you guys. There's all this deep learning source code out there. It's open bar for open source right now. All this goodness is pouring in. Google's donating code, you guys are donating code. It used to be like, you had to build your code from scratch. Borrow here and there, and share in open source. Now it's a tsunami of greatness, so I'm just going to build my own deep learning. How do customers do that? It's too hard. >> You are right on the point to the next challenge of deep learning, which I believe is out there. Because we have so many efforts to speed up the infrastructure, we have so many open source libraries. So now the question is, okay, I have my application at hand. What should I choose? What is the right compute node to the deep learning? Everybody use GPUs, but is it true for all models? How many GPUs do I need? What is the optimal number of nodes in the cluster? And we have a research effort towards to answer those questions as well. >> And a breathalyzer for all the drunk coders out there, open bar. I mean, a lot of young kids are coming in. This is a great opportunity for everyone. And in all seriousness, we need algorithms for the algorithms. >> And I think that's where it's so fascinating. We think of some classes of things, like recognizing written handwriting, recognizing voice, but when we want to apply machine learning and algorithms to the volume of sensor data, so that every manufactured item, and not only every item we manufacture, but every factory that can be fully instrumented with machine learning understanding how it can be optimized. And then, what of the business processes that are feeding that factory? And then, what are the overall economic factors that are feeding that business? And instrumenting and having this learning, this unblinking, unbiased eye examining to find those hidden correlations, those hidden connections, that could yield a very much more efficient system at every level of human enterprise. >> And the data's more diverse now than ever. I'm sorry to interrupt, but in Voice you mentioned you saw Siri, you see Alexa, you see Voice as one dataset. Data diversity's massive, so more needles, more types of needles than ever before. >> In that example that you gave, you need a domain expert. And there's plenty of those, but you also need a big brain to build the model, and train the model, and iterate. And there aren't that many of those. Is the state of machine learning and AI going to get to the point where that problem will solve itself, or do we just need to train more big brains? >> Actually, one of the advantages of deep learning that you don't need that much effort from the domain experts anymore, from the step which was called future engineering, like, what do you do with your data before you throw machine learning algorithm into that? So they're, pretty thing, cool thing about deep learning, artificial neural network, that you can throw almost raw data into that. And there are some examples out there, that the people without any knowledge in medicine won the competition of the drug recognition by applying deep neural networks to that, without knowing all the details about their connection between proteins, like that. Not domain experts, but they still were able to win that competition. Just because algorithm that good. >> Kirk, I want to ask you a final question before we break in the segment because, having spent nine years of my career at HP in the '80s and '90s, it's been well-known that there's been great research at HP. The R&D has been spectacular. Not too much R, I mean, too much D, not enough applied, you mention you're bringing that to market faster, so, the question is, what should customers know about Hewlett Packard Labs today? Your mission, obviously the memory-centric is the key thing. You got The Machine, you got the Memristor, you got a novel way of looking at things. What's the story that you'd like to share? Take a minute, close out the segment and share Hewlett Packard Labs' mission, and what expect to see from you guys in terms of your research, your development, your applications. What are you guys bringing out of the kitchen? What's cooking in the oven? >> I think for us, it is, we've been given an opportunity, an opportunity to take all of those ideas that we have been ruminating on for five, 10, maybe even 15 years. All those things that you thought, this is really something. And we've been given the opportunity to build a practical working example. We just turned on the prototype with more memory, more computation addressable simultaneously than anyone's ever assembled before. And so I think that's a real vote of confidence from our leadership team, that they said, "Now, the ideas you guys have, "this is going to change the way that the world works, "and we want to see you given every opportunity "to make that real, and to make it effective." And I think everything that Hewlett Packard Enterprise has done to focus the company on being that fantastic infrastructure, provider and partner is just enabling us to get this innovation, and making it meaningful. I've been designing printed circuit boards for 28 years, now, and I must admit, it's not as, you know, it is intellectually stimulating on one level, but then when you actually meet someone who's changing the face of Alzheimer's research, or changing the way that we produce energy as a society, and has an opportunity to really create a more sustainable world, then you say, "That's really worth it." That's why I get up, come to Labs every day, work with fantastic researchers like Natalia, work with great customers, great partners, and our whole supply chain, the whole team coming together. It's just spectacular. >> Well, congratulations, thanks for sharing the insight on theCUBE. Natalia, thank you very much for coming on. Great stuff going on, looking forward to keeping the progress and checking in with you guys. Always good to see what's going on in the Lab. That's the headroom, that's the future. That's the bridge to the future. Thanks for coming in theCUBE. Of course, more CUBE coverage here at HP Discover, with the keynotes coming up. Meg Whitman on stage with Antonio Neri. Back with more live coverage after this short break. Stay with us. (energetic techno music)

>> Jeff: Hello, everybody, and thank you for joining us today for the virtual Vertica BDC 2020. Today's breakout session is entitled "A Deep Dive "into the Vertica Mangement Console Enhancements and Roadmap." I'm Jeff Healey of Vertica Marketing. I'll be your host for this breakout session. Joining me are Bhavik Gandhi and Natalia Stavisky from Vertica engineering. But before we begin, I encourage you to submit questions or comments during the virtual session. You don't have to wait, just type your question or comment in the question box below the slides and click submit. There will be a Q and A session at the end of the presentation. We'll answer as many questions as we're able to during that time. Any questions we don't address, we'll do our best to answer them offline. Alternatively visit Vertica Forums at forum.vertica.com. Post your question there after the session. Our engineering team is planning to join the forums to keep the conversation going well after the event. Also, a reminder that you can maximize the screen by clicking the double arrow button in the lower right corner of the slides. And yes, this virtual session is being recorded and will be available to you on demand this week. We'll send you a notification as soon as it's ready. Now let's get started. Over to you, Bhavik. >> Bhavik: All right. So hello, and welcome, everybody doing this presentation of "Deep Dive into the Vertica Management Console Enhancements and Roadmap." Myself, Bhavik, and my team member, Natalia Stavisky, will go over a few useful announcements on Vertica Management Console, discussing a few real scenarios. All right. So today we will go forward with the brief introduction about the Management Console, then we will discuss the benefits of using Management Console by going over a couple of user scenarios for the query taking too long to run and receiving email alerts from Management Console. Then we will go over a few MC features for what we call Eon Mode databases, like provisioning and reviving the Eon Mode databases from MC, managing the subcluster and understanding the Depot. Then we will go over some of the future announcements on MC that we are planning. All right, so let's get started. All right. So, do you want to know about how to provision a new Vertica cluster from MC? How to analyze and understand a database workload by monitoring the queries on the database? How do you balance the resource pools and use alerts and thresholds on MC? So, the Management Console is basically our answer and we'll talk about its capabilities and new announcements in this presentation. So just to give a brief overview of the Management Console, who uses Management Console, it's generally used by IT administrators and DB admins. Management Console can be used to monitor both Eon Mode and Enterprise Mode databases. Why to use Management Console? You can use Management Console for provisioning Vertica databases and cluster. You can manage the already existing Vertica databases and cluster you have, and you can use various tools on Management Console like query execution, Database Designer, Workload Analyzer, and set up alerts and thresholds to get notified by some of your activities on the MC. So let's go over a few benefits of using Management Console. Okay. So using Management Console, you can view and optimize resource pool usage. Management Console helps you to identify some critical conditions on your Vertica cluster. Additionally, you can set up various thresholds thresholds in MC and get other data if those thresholds are triggered on the database. So now let's dig into the couple of scenarios. So for the first scenario, we will discuss about queries taking too long and using workload analyzer to possibly help to solve the problem. In the second scenario, we will go over alert email that you received from your Management Console and analyzing the problem and taking required actions to solve the problem. So let's go over the scenario where queries are taking too long to run. So in this example, we have this one query that we are running using the query execution on MC. And for some reason we notice that it's taking about 14.8 seconds seconds to execute this query, which is higher than the expected run time of the query. The query that we are running happens to be the query used by MC during the extended monitoring. Notice that the table name and the schema name which is ds_requests_issued, and, is the schema used for extended monitoring. Now in 10.0 MC we have redesigned the Workload Analyzer and Recommendations feature to show the recommendations and allow you to execute those recommendations. In our example, we have taken the table name and figured the tuning descriptions to see if there are any tuning recommendations related to this table. As we see over here, there are three tuning recommendations available for that table. So now in 10.0 MC, you can select those recommendations and then run them. So let's run the recommendations. All right. So once recommendations are run successfully, you can go and see all the processed recommendations that you have run previously. Over here we see that there are three recommendations that we had selected earlier have successfully processed. Now we take the same query and run it on the query execution on MC and hey, it's running really faster and we see that it takes only 0.3 seconds to run the query and, which is about like 98% decrease in original runtime of the query. So in this example we saw that using a Workload Analyzer tool on MC you can possibly triage and solve issue for your queries which are taking to long to execute. All right. So now let's go over another user scenario where DB admin's received some alert email messages from MC and would like to understand and analyze the problem. So to know more about what's going on on the database and proactively react to the problems, DB admins using the Management Console can create set of thresholds and get alerted about the conditions on the database if the threshold values is reached and then respond to the problem thereafter. Now as a DB admin, I see some email message notifications from MC and upon checking the emails, I see that there are a couple of email alerts received from MC on my email. So one of the messages that I received was for Query Resource Rejections greater than 5, pool, midpool7. And then around the same time, I received another email from the MC for the Failed Queries greater than 5, and in this case I see there are 80 failed queries. So now let's go on the MC and investigate the problem. So before going into the deep investigation about failures, let's review the threshold settings on MC. So as we see, we have set up the thresholds under the database settings page for failed queries in the last 10 minutes greater than 5 and MC should send an email to the individual if the threshold is triggered. And also we have a threshold set up for queries and resource rejections in the last five minutes for midpool7 set to greater than 5. There are various other thresholds on this page that you can set if you desire to. Now let's go and triage those email alerts about the failed queries and resource rejections that we had received. To analyze the failed queries, let's take a look at the query statistics page on the database Overview page on MC. Let's take a look at the Resource Pools graph and especially for the failed queries for each resource pools. And over to the right under the failed query section, I see about like, in the last 24 hours, there are about 6,000 failed queries for midpool7. And now I switch to view to see the statistics for each user and on this page I see for User MaryLee on the right hand side there are a high number of failed queries in last 24 hours. And to know more about the failed queries for this user, I can click on the graph for this user and get the reasons behind it. So let's click on the graph and see what's going on. And so clicking on this graph, it takes me to the failed queries view on the Query Monitoring page for database, on Database activities tab. And over here, I see there are a high number of failed queries for this user, MaryLee, with the reasons stated as, exceeding high limit. To drill down more and to know more reasons behind it, I can click on the plus icon on the left hand side for each failed queries to get the failure reason for each node on the database. So let's do that. And clicking the plus icon, I see for the two nodes that are listed, over here it says there are insufficient resources like memory and file handles for midpool7. Now let's go and analyze the midpool7 configurations and activities on it. So to do so, I will go over to the Resource Pool Monitoring view and select midpool7. I see the resource allocations for this resource pool is very low. For example, the max memory is just 1MB and the max concurrency is set to 0. Hmm, that's very odd configuration for this resource pool. Also in the bottom right graph for the resource rejections for midpool7, the graph shows very high values for resource rejection. All right. So since we saw some odd configurations and odd resource allocations for midpool7, I would like to see when this resource, when the settings were changed on the resource pools. So to do this, I can preview the audit logs on, are available on the Management Console. So I can go onto the Vertica Audit Logs and see the logs for the resource pool. So I just (mumbles) for the logs and figuring the logs for midpool7. I see on February 17th, the memory and other attributes for midpool7 were modified. So now let's analyze the resource activity for midpool7 around the time when the configurations were changed. So in our case we are using extended monitoring on MC for this database, so we can go back in time and see the statistics over the larger time range for midpool7. So viewing the activities for midpool7 around February 17th, around the time when these configurations were changed, we see a decrease in resource pool usage. Also, on the bottom right, we see the resource rejections for this midpool7 have an increase, linear increase, after the configurations were changed. I can select a point on the graph to get the more details about the resource rejections. Now to analyze the effects of the modifications on midpool7. Let's go over to the Query Monitoring page. All right, I will adjust the time range around the time when the configurations were changed for midpool7 and completed activities queries for user MaryLee. And I see there are no completed queries for this user. Now I'm taking a look at the Failed Queries tab and adjusting the time range around the time when the configurations were changed. I can do so because we are using extended monitoring. So again, adjusting the time, I can see there are high number of failed queries for this user. There about about like 10,000 failed queries for this user after the configurations were changed on this resource pool. So now let's go and modify the settings since we know after the configurations were changed, this user was not able to run the queries. So you can change the resource pool settings of using Management Console's database settings page and under the Resource Pools tab. So selecting the midpool7, I see the same odd configurations for this resource pool that we saw earlier. So now let's go and modify it, the settings. So I will increase the max memory and modify the settings for midpool7 so that it has adequate resources to run the queries for the user. Hit apply on the right hand top to see the settings. Now let's do the validation after we change the resource pool attributes. So let's go over to the same query monitoring page and see if MaryLee user is able to run the queries for midpool7. We see that now, after the configuration, after the change, after we changed the configuration for midpool7, the user can run the queries successfully and the count for Completed Queries has increased after we modified the settings for this midpool7 resource pool. And also viewing the resource pool monitoring page, we can validate that after the new configurations for midpool7 has been applied and also the resource pool usage after the configuration change has increased. And also on the bottom right graph, we can see that the resource rejections for midpool7 has decreased over the time after we modified the settings. And since we are using extended monitoring for this database, I can see that the trend in data for these resource pools, the before and after effects of modifying the settings. So initially when the settings were changed, there were high resource rejections and after we again modified the settings, the resource rejections went down. Right. So now let's go work with the provisioning and reviving the Eon Mode Vertica database cluster using the Management Console on different platform. So Management Console supports provisioning and reviving of Eon Mode databases on various cloud environments like AWS, the Google Cloud Platform, and Pure Storage. So for Google, for provisioning the Vertica Management Console on Google Cloud Platform you can use launch a template. Or on AWS environment you can use the cloud formation templates available for different OS's. Once you have provisioned Vertica Management Console, you can provision the Vertica cluster and databases from MC itself. So you can provision a Vertica cluster, you can select the Create new database button available on the homepage. This will open up the wizard to create a new database and cluster. In this example, we are using we are using the Google Cloud Platform. So the wizard will ask me for varius authentication parameters for the Google Cloud Platform. And if you're on AWS, it'll ask you for the authentication parameters for the AWS environment. And going forward on the Wizard, it'll ask me to select the instance Type. I will select for the new Vertica cluster. And also provide the communal location url for my Eon Mode database and all the other preferences related to the new cluster. Once I have selected all the preferences for my new cluster I can preview the settings and I can hit, if I am, I can hit Create if all looks okay. So if I hit Create, this will create a new, MC will create a new GCP instances because we are on the GCP environment in this example. It will create a cluster on this instance, it'll create a Vertica Eon Mode Database on this cluster. And it will, additionally, you can load the test data on it if you like to. Now let's go over and revive the existing Eon Mode database from the communal location. So you can do it the same using the Management Console by selecting the Revive Eon Mode database button on the homepage. This will again open up the wizard for reviving the Eon Mode database. Again, in this example, since we are using GCP Platform, it will ask me for the Google Cloud storage authentication attributes. And for reviving, it will ask me for the communal location so I can enter the Google Storage bucket and my folder and it will discover all the Eon Mode databases located under this folder. And I can select one of the databases that I would like to revive. And it will ask me for other Vertica preferences and for this video, for this database reviving. And once I enter all the preferences and review all the preferences I can hit Revive the database button on the Wizard. So after I hit Revive database it will create the GCP instances. The number of GCP instances that I created would be seen as the number of hosts on the original Vertica cluster. It will install the Vertica cluster on this data, on this instances and it will revive the database and it will start the database. And after starting the database, it will be imported on the MC so you can start monitoring on it. So in this example, we saw you can provision and revive the Vertica database on the GCP Platform. Additionally, you can use AWS environment to provision and revive. So now since we have the Eon Mode database on MC, Natalia will go over some Eon Mode features on MC like managing subcluster and Depot activity monitoring. Over to you, Natalia. >> Natalia: Okay, thank you. Hello, my name is Natalia Stavisky. I am also a member of Vertica Management Console Team. And I will talk today about the work I did to allow users to manage subclusters using the Management Console, and also the work I did to help users understand what's going on in their Depot in the Vertica Eon Mode database. So let's look at the picture of the subclusters. On the Manage page of Vertica Management Console, you can see here is a page that has blue tabs, and the tab that's active is Subclusters. You can see that there are two subclusters are available in this database. And for each of the subclusters, you can see subcluster properties, whether this is the primary subcluster or secondary. In this case, primary is the default subcluster. It's indicated by a star. You can see what nodes belong to each subcluster. You can see the node state and node statistics. You can also easily add a new subcluster. And we're quickly going to do this. So once you click on the button, you'll launch the wizard that'll take you through the steps. You'll enter the name of the subcluster, indicate whether this is secondary or primary subcluster. I should mention that Vertica recommends having only one primary subcluster. But we have both options here available. You will enter the number of nodes for your subcluster. And once the subcluster has been created, you can manage the subcluster. What other options for managing subcluster we have here? You can scale up an existing subcluster and that's a similar approach, you launch the wizard and (mumbles) nodes. You want to add to your existing subcluster. You can scale down a subcluster. And MC validates requirements for maintaining minimal number of nodes to prevent database shutdown. So if you can not remove any nodes from a subcluster, this option will not be available. You can stop a subcluster. And depending on whether this is a primary subcluster or secondary subcluster, this option may be available or not available. Like in this picture, we can see that for the default subcluster this option is not available. And this is because shutting down the default subcluster will cause the database to shut down as well. You can terminate a subcluster. And again, the MC warns you not to terminate the primary subcluster and validates requirements for maintaining minimal number of nodes to prevent database shutdown. So now we are going to talk a little more about how the MC helps you to understand what's going on in your Depot. So Depot is one of the core of Eon Mode database. And what are the frequently asked questions about the Depot? Is the Depot size sufficient? Are a subset of users putting a high load on the database? What tables are fetched and evicted repeatedly, we call it "re-fetched," in Depot? So here in the Depot Activity Monitoring page, we now have four tabs that allow you to answer those questions. And we'll go a little more in detail through each of them, but I'll just mention what they are for now. At a Glance shows you basic Depot configuration and also shows you query executing. Depot Efficiency, we'll talk more about that and other tabs. Depot Content, that shows you what tables are currently in your Depot. And Depot Pinning allows you to see what pinning policies have been created and to create new pinning policies. Now let's go through a scenario. Monitoring performance of workloads on one subcluster. As you know, Eon Mode database allows you to have multiple subclusters and we'll explore how this feature is useful and how we can use the Management Console to make decisions regarding whether you would like to have multiple subclusters. So here we have, in my setup, a single subcluster called default_subcluster. It has two users that are running queries that are accessing tables, mostly in schema public. So the query started executing and we can see that after fetching tables from Communal, which is the red line, the rest of the time the queries are executing in Depot. The green line is indicating queries running in Depot. The all nodes Depot is about 88% full, a steady flow, and the depot size seems to be sufficient for query executions from Depot only. That's the good case scenario. Now at around 17 :15, user Sherry got an urgent request to generate a report. And at, she started running her queries. We can see that picture is quite different now. The tables Sherry is querying are in a different schema and are much larger. Now we can see multiple lines in different colors. We can see a bunch of fetches and evictions which are indicated by blue and purple bars, and a lot of queries are now spilling into Communal. This is the red and orange lines. Orange line is an indicator of a query running partially in Depot and partially getting fetched from Communal. And the red line is data fetched from Communal storage. Let's click on the, one of the lines. Each data point, each point on the line, it'll take you to the Query Details page where you can see more about what's going on. So this is the page that shows us what queries have been run in this particular time interval which is on top of this page in orange color. So that's about one minute time interval and now we can see user Sherry among the users that are running queries. Sherry's queries involve large tables and are running against a different schema. We can see the clickstream schema in the name of the, in part of the query request. So what is happening, there is not enough Depot space for both the schema that's already in use and the one Sherry needs. As a result, evictions and fetches have started occurring. What other questions we can ask ourself to help us understand what's going on? So how about, what tables are most frequently re-fetched? So for that, we will go to the Depot Efficiency page and look at the middle, the middle chart here. We can see the larger version of this chart if we expand it. So now we have 10 tables listed that are most frequently being re-fetched. We can see that there is a clickstream schema and there are other schemas so all of those tables are being used in the queries, fetched, and then there is not enough space in the Depot, they getting evicted and they get re-fetched again. So what can be done to enable all queries to run in Depot? Option one can be increase the Depot size. So we can do this by running the following queries, which (mumbles) which nodes and storage location and the new Depot size. And I should mention that we can run this query from the Management Console from the query execution page. So this would have helped us to increase the Depot size. What other options do we have, for example, when increasing Depot size is not an option? We can also provision a second subcluster to isolate workloads like Sherry's. So we are going to do this now and we will provision a second subcluster using the Manage page. Here we're creating subcluster for Sherry or for workloads like hers. And we're going to create a (mumbles). So Sherry's subcluster has been created. We can see it here, added to the list of the subclusters. It's a secondary subcluster. Sherry has been instructed to use the new SherrySubcluster for her work. Now let's see what happened. We'll go again at Depot Activity page and we'll look at the At a Glance tab. We can see that around >> 18: 07, Sherry switched to running her queries on SherrySubcluster. On top of this page, you can see subcluster selected. So we currently have two subclusters and I'm looking, what happened to SherrySubcluster once it has been provisioned? So Sherry started using it and the lines after initial fetching from Depot, which was from Communal, which was the red line, after that, all Sherry's queries fit in Depot, which is indicated by green line. Also the Depot is pretty full on those nodes, about 90% full. But the queries are processed efficiently, there is no spilling into Communal. So that's a good case scenario. Let's now go back and take a look at the original subcluster, default subcluster. So on the left portion of the chart we can see multiple lines, that was activity before Sherry switched to her own designated subcluster. At around 18:07, after Sherry switched from the subcluster to using her designated subcluster, there is no, she is no longer using the subcluster, she is not putting a load in it. So the lines after that are turning a green color, which means the queries that are still running in default subcluster are all running in Depot. We can also see that Depot fetches and evictions bars, those purple and blue bars, are no longer showing significant numbers. Also we can check the second chart that shows Communal Storage Access. And we can see that the bars have also dropped, so there is no significant access for Communal Storage. So this problem has been solved. Each of the subclusters are serving queries from Depot and that's our most efficient scenario. Let's also look at the other tabs that we have for Depot monitoring. Let's look at Depot Efficiency tab. It has six charts and I'll go through each one of them quickly. Files Reads by Location gives an indicator of where the majority of query execution took place in Depot or in Communal. Top 10 Re-Fetches into Depot, and imagine the charts earlier in our user case, it shows tables that are most frequently fetched and evicted and then fetched again. These are good candidates to get pinned if increasing Depot size is not an option. Note that both of these charts have an option to select time interval using calendar widget. So you can get the information about the activity that happened during that time interval. Depot Pinning shows what portion of your Depot is pinned, both by byte count and by table count. And the three tables at the bottom show Depot structure. How long tables stay in Depot, we would like tables to be fetched in Depot and stay there for a long time, how often they are accessed, again, the tables in Depot, we would like to see them accessed frequently, and what the size range of tables in Depot. Depot Content. This tab allows us to search for tables that are currently in Depot and also to see stats like table size in Depot. How often tables are accessed and when were they last accessed. And the same information that's available for tables in Depot is also available on projections and partition levels for those tables. Depot Pinning. This tab allows users to see what policies are currently existing and so you can do this by clicking on the first little button and click search. This'll show you all existing policies that are already created. The second option allows you to search for a table and create a policy. You can also use the action column to modify existing policies or delete them. And the third option provides details about most frequently re-fetched tables, including fetch count, total access count, and number of re-fetched bytes. So all this information can help to make decisions regarding pinning specific tables. So that's about it about the Depot. And I should mention that the server team also has a very good presentation on the, webinar, on the Eon Mode database Depot management and subcluster management. that strongly recommend it to attend or download the slide presentation. Let's talk quickly about the Management Console Roadmap, what we are planning to do in the future. So we are going to continue focusing on subcluster management, there is still a lot of things we can do here. Promoting/demoting subclusters. Load balancing across subclusters, scheduling subcluster actions, support for large cluster mode. We'll continue working on Workload Analyzer enhancement recommendation, on backup and restore from the MC. Building custom thresholds, and Eon on HDFS support. Okay, so we are ready now to take any questions you may have now. Thank you.

from Houston Texas extracting the signal from the noise it's the cute coverage Grace Hopper celebration of women in computing now your host John furrier and Jeff fridge okay welcome back everyone we are here live in Houston Texas for the Grace Hopper celebration of women in computing this is SiliconANGLE media's the cube our flagship program we go out to the events and extract the simla noise i'm john ferry the founder of SiliconANGLE join with Jeff Frick general manager of the cube our next guest is Lisa Dougal who's the chief diversity officer pwc consulting welcome to the cube thank you very much great to see you great to chat with you before we came on we talked about you were at Carnegie Mellon back in the 80s and we just had Eileen big enough for it to it another 80s throwback like me in sheb back to the 80s hot tub time machine whatever you want to call it it's a lot of fun so thanks for spending some time with us oh my pleasure so first what are you working on so that's the first point we've learned that's a good question to ask what are you working on what am i working on so for me personally I do a number of different things right as my role is chief diversity officer I am creating and evolving and implementing programs that help all kinds of diversity in the workplace which ranges from women to minorities to men as well which is one of our big focus areas right as a partner in the practice i'm also a retail consumer partner so I work with retail and consumer clients on transforming their businesses from strategy to execution digital transformations hot right now Adam everything is being automated I mean everything's addressable now Internet of Things creates absolutely % data acquisition it does but I think at the same time it's created such a wealth of I will call it information old school or data its recent project right I think companies are struggling with how do you parse through how do you tell the story how do you figure out a what the data is telling you if you take the consumer industry for one right they've got huge amounts of consumer data now the question is how do you use it how you turn it into innovation one of the things you were mentioning before you came on was that you did a thesis at Carnegie Mellon back in the eighties where you ready to say a computer science major but everyone had the code which great paid back in the 80s and maybe we should reinstitute that across the university I agree I think everything went should coach likes math and sciences to me I think a requisite skill for everybody but you say that these are supposed decision-making using computers now fast forward to today where we were just chatting about for the first time in modern in business history you can actually measure everything so no more excuses if you could actually measure everything right so the question becomes what do you want to measure right yeah so what does that do with a business how does that change and I think it's a combination of measurement which just looks historical and that's important right with predictive and right where the world is going it's predictive analytics behavioral analytics right because that enables us to figure out how we want to change we're only ever looking backwards we had a static point in time yeah and that's informative and you need that and as we talked before you need to be able to parse through the data and decide which is relevant and which is really the lever you want to pull but I think more and more we're seeing companies doing data modeling and data predictive analytics on just about everything right right and Merv Adrian loves to talk about data in motion from gartner and you know it's no longer good enough to have it look at it then decide what you're going to do now really was spark and some of the new technologies you actually have an opportunity to look at the data in motion in a transaction in a retail environment and change change the transaction midstream to hopefully get to a better out absolutely so what you seeing kind of out in the in the world of some of these more advanced retailers and some of the things I think that's happening i think the ability to drop coupons as people walk by the aisle is more and more prevalent right not just any coupon but we know you buy a lot of milk right i think you're going to see more and more price changing based on the consumer i know you you've been into my store you're a loyal customer I'll pop you the milk at this price where somebody else might pay a higher price I think the world is open in terms of how these companies are using not just the data they collect on the product and the technologies but also on you as the individual least I want to get your thoughts on a concept that we've been kind of gleaming out of the data here at Grace Hopper and other events we've been to around women in computing but more importantly also computer science and that there's a lot of different semantics people argue about women versus ladies this versus that there's so many different you know biases mean I'm biased whatever all that stuff's happening but one constant in all this is that these two debt variables transparency and always learning and that seems to be a driver of a lot of change here and you mentioned digital transformation what are you seeing out there that's really driving the opportunities around transparency you can save data access you have data then things are transparent always be learning this new opportunities so those seems to be a big pivot points here at this event here where there's a lot of opportunities there's a subtle conversation of not just the pay thing and the gender equality on pay but opportunities is the big theme we're seeing here absolutely I am really energized by being here right first of all to see so many young women all passionate about technology and computing and really being inserted in the right ways you know I've had women come up to me even on the escalator shake my hand as a hello you're from pricewaterhousecoopers let me ask you what you do during your day right I think in my day a there was no place to go and even if you did you were trying to navigate a very different world and you were trying to perhaps not be you but be somebody else right how do you fit into the man's world I used to watch all sports all weekend so I can make sure I could participate in office conversation when I got in on Monday mornings right I think to hear the conversations that the women are having that are very technology driven but also very much authentic to who they are is where we're going see if you were a young lady in tech now you actually program the fantasy games so that you'd win the game everywhere that's right you could write the code this is but there's a lot of coding a lot of developers here phenomenal growth in develops we just had a young girl just graduated she's phenomenal Natalia and she got into it she started in journalism major and second year in she switched into computer science because she was tinkering with wearables which is terrific right one of the conversations I like to have with our young women about PwC in particular but a lot of parts of the industry the ability to combine industry or sector knowledge with the technology right so I was talking to one women who said well you know I just switched out of pre-med I really like medicine but I got into coding and I simply have you thought about you know the whole arena of the health care industry is dramatically changing right we're moving to the point where we have you know patient information hospital information drug trial information we can integrate all that you could stay with healthcare and still do technology and coding and she's looking at me like she'd never thought about the revelation you said early undulation the old days you try to be someone else try to fit into a man's world but now you're saying you know just the app just follow your passion and this technology behind it interesting enough is also an effect on the men like I had a Facebook post on my flight down here at the Wi-Fi on the plane and i typed in my facebook friends hey real question is a politically incorrect to say I love women in tech I kind of put that out there is kind of a link bait but all sudden the arguments were weighed politically correct love is for versions of love's like argument and wedding Gary deep hey very deep but the one comment was just be yourself and I think I tell our women that all the time and all our people right but i think this the shift to the workplace openness where you can be authentic and i find often are young women in particular get guidance from mentors who are men and they try to emulate that and some of that is good but you have to emulate that while being authentic to who you are otherwise you run that risk of perhaps being perceived in authentic or you know it comes off a little bit too can write what's your best advice to men because one of the things that we seeing is a trend now and certainly is that men inclusion is also into the conversation absolutely big thing we are doing that as a firm both in the US and globally we're a ten-by-ten impact sponsor for he for she which is the UN's initiative with companies governments and not-for-profits to engage men in a conversation about raising awareness around women and for us it's women in the workplace right so there really a couple of things I think men can do one is listen and actively engage with the women and not just women at your level women who are Millennials as well if you can't of not comfortable having that conversation which I know many with women and men both aren't it's hard to put yourself in their shoes right the second is to really be an advocate right think about when you walk into meetings who's not in the room are the people looking all like you what do you do about that right and i think that the third is make it personal you know be involved and know what's going on and know how you could help it seems so simple right when you just lay it out there right those are not complicated concepts but but to put them in practices is you know it takes an active you know kind of thinking about it right to really make it happen to impact change it does and i think more it is natural for people to gravitate to people who are like them particularly in the workspace we get very comfortable in our own let's call them echo chambers and then you move with your echo chamber and your echo chamber might have a little diversity but likely it doesn't have a lot of generational diversity it may or may not have all kinds of racial ethnic gender diversity and so you might meet somebody on the outside who's a little different but you go back to your go tues who are still in your echo chamber so I think the goal is to get into multiple a few echo chambers right also I also comfort zone right i mean people like what's familiar to them and pushing the comfort zone barrier is one issue right now happy young come to be uncomfortable be comfortable and the uncomfortable how is that right what people should look for I mean and everyone has their own struggles and journeys what how did people cope it so I often to have this conversation with methanol how do I talk to women about being women I said well that's probably not the first conversation you should be having right talk to them about who they are and what's important to you and then the relationship you have to build what we call familiarity comfort and trust and once you've built that you can have a conversation perhaps about what a woman's plans are if she's pregnant but you can't just walk in and taught me the for that yeah you can't blurt it out right thank you thanks off at not a walk not a good icebreaker yeah yeah so Lisa you know there's a lot of talk about what's the right thing to do what is right meaning it's the right thing to do in terms of morally and as a human being to include people but really there's there's a bottom line positive impact to there's a better outcome impact and pwc you guys do a lot of analysis you work a lot of companies so there's some studies you can share some some facts or figures that you guys have discovered about how there's really great bottom line better decisions better products better profitability when you have a diverse point of view that you bring to a problem set absolutely there are number of different ways to look at that I think you're right it is the right thing to do the moral thing to do people want to feel good about it but at the end of the day we know that diversity is good for business performance right and there are a number of studies out there that talk about board composition and how you know now bored women on boards has been legislated in enough countries around the world for long enough now you can correlate long-term 10-15 year performance with the performance of those companies and we see that those companies perform better right you can look at just the diversity I mean another angle of looking at it is we do a lot of work with Millennials in the millennial studies right and people coming off a campus are more Geographic gender ethnic minority diverse than any generations we've seen at a very long time right there more women coming off of campus in general than men right now and they're doing very well right so there's also the zero-sum game that says if we don't figure out how to accommodate a track promote retain women then we're not going to be able to get the best of the best of the workforce and you become at a competitive disadvantage well it's quality that's the competitive advantage is the quality that you get with the diversity absolutely how do you manage that process because some would say diversity slows things down because you have different perspectives but the outputs higher quality high equality and more innovation right and one of the things we like to do is talk about diversity and a number of different angles so there's race gender sexual orientation there's also in our business diversity of degrees so we have coders working with mba is working with lawyers doctors strategist and part of that is the way you get the thinking and the most innovative solutions to your problems and I think when you begin to develop and to find it that way there are places for more people to get on the wheel so to speak right everybody is thinking about diversity not just you look different or you experience but you bring a different perspective to the problem because you have a different background where you grow up and what you studied it's just it's just funny that you know in being diverse you're actually leveraging people's biases to get to a better solution absolutely perfect all the way around that's right and i think that there's a movement now and we're really moving from thinking about being equal to thinking about being equitable right equal would say if you have three kids peering over a fence ones four foot ones five-foot 16 foot give them all in one foot box well that's not going to get the forefoot guy over the fence right what you really have to do is give them each a size box that they need right so the six-foot kid probably doesn't need a box at all if it's a five-foot fence right the 5-foot kid might need a little stepstool and the forfeit kid probably needs a large cube right right that's being equitable it's not necessary to me out well based on the outcome based on the album about the objective right versus some statistical equitable correct so I think in business we're moving more to looking at that outcome based heck with biddle equity being equitable across outcomes equitable thank you not just being equal because I think for a long term it was treat everybody the same and that's diversity it's really appreciate everybody for their just as differences and let them play to their strengths right and use the data science tools available Go Daddy put out the survey results of their salaries to you seeing the University of Virginia Professor Brian gave a keynote today about the software that they're building an open source for tooling but the date is going to be key but at the end of the day management drives the outcome objective so I'm Celeste someone at a senior level who's had a good journey from the 80 Eileen big and talk about the same thing you're now at the top of the pyramid the flywheels developing there's some good on in migration with women coming into the field house the balance how's that flywheel working for the mentoring the pipeline in the operational I'd say I give you one example right so we have a women in technology what started as a program it's now a part of our business right we started about two and a half years ago with 30 women who are trying to figure out in technology you give you a long term implementation projects for you know six months a year two years and only operate in the same echo chamber right so how do you network with other women how do you meet them it's now 1400 people strong and one of the pillars of it is a mentorship program we had and it doesn't sound like a lot but see from where you start right increase if we started with needing having about 50 50 women mentored right we're up to hundreds of women being mentored and last time we opened the program we had 150 leaders not just we had other people but leaders sign up within the first few days to mentor the women so in my mind that's success that success reason I didn't need to promise my job good job on your older thank you taking you for that network effect there's an app for that now the network effectors are dynamic now so coming back to the theory of socialization and social theory as you get a network effect going on there's a good social vibe going on talk about that dynamic it's kind of qualitative and then be might be some numbers so save it but talk about that the the network effect of that viral growth if you will I think you sort of have it's now a important and good and rewarded thing to do right but I also think there's a millennial factor there yeah right so what we've been able to see is as our tech women come in off a campus they're beginning to get opportunities that change the game around women in the community right so we brought a number of two-year three-year out women with us and have them help us in the planning of being here all the way from designing our website to putting together the booth to submitting and speaking at so they got speaker slots which gives them amazing exposure with then sentenced that social dynamic in a number of ways right you have them wanting to other people wanting to emulate it you have leaders reaching out to me and say wow we didn't know Emily you know Emily did that that is great right she spoke to 900 people yesterday and so that changes the social landscape acceptable it certainly does it's great amplification so as we wrap here at Lisa I think that's a great segue talk about the Grace Hopper celebration of women in computing it's a very different kind of conference it's a very different kind of feel why is it important to pwc why do you guys invest in this show and you know the example you caves just a great lead into it I think it's for a number of reasons it's a great source of recruits right so so we want to be here we want to meet the young people coming off of campus so maybe we might not meet in our structured campus environment right I think the second is it's a great opportunity for our young women to promote and develop themselves and gain skills that we would never gain I think the third is just to empower our women just like being here and even the emails i'm getting from our women who are not here and our men who are not here the fact that we are here has sort of had a little bit of a viral offensive foam oh you're missing out you're missing out it's an amazing experience it's really helped put in some ways women in technology in a little different league right a lot of the alliances and a lot of the conference's we do are we do 15 major conferences now and we support leadership for women events at all of them but this is one of the few that's not alliance space it's not being at SI p with us AP or being an owl with Oracle which are great things for us to do but this is for the women about the women and the development of the women it's an exciting time and we're excited to document and thanks for spending the time sharing your insights and data and perspective here on the cube well thank you so much John and jeff bennett me having me whereas our pleasure was so inspired so really awesome and if you want to be part of the cube we are hiring looking for women digital scientists data analyst on-air host and we've been shamed a little bit for having an all-male team here I was just gonna ask ya we are looking for powerful strong smart women who want to join the cube we're hiring so contact us offline thanks for watching me right back with more live coverage here in Houston Texas at the Grace Hopper celebration be right back